A pathologist's analysis of histology images is primarily subjective. Since health care practice is evidence-based, it is crucial to have reproducible methods. The subjective nature of many diagnostic tasks in anatomic pathology and cytology is, however, known to cause reproducibility problems, i.e. high inter- and intra-observer variability in many diagnostic situations. Also, efficiency improvements are highly needed.
Today there are many scanners capable of producing high-quality digital images from microscopy glass slides. See, e.g., Rojo et al., Critical comparison of 31 commercially available slide systems in pathology, Int J. Surg. Pathol., 2006; 14(4):285-305. This digital practice is often called “WSI” or “virtual microscopy” for cytopathology. The resulting digital images can be very large, for instance 30,000×40,000 pixels, 100,000×100,000 pixels or more. In histology, a two-dimensional (2D) image often suffices, but there is also the possibility to produce slices across the depth of the tissue section, creating a three-dimensional (3D) dataset even though the extent in the z direction can be far different from the x-y directions.
A fundamental part of the diagnostic exploration in both analog microscopes and WSI viewers is to switch between different magnification levels. In the microscope, this is done by physically switching lenses and all WSI viewers offer some kind of magnification interaction. At different magnification levels, different characteristics can be studied. For example, at low magnification, tissue structure and major tissue components can be seen. One part of the pathologist's work at this level is to determine regions of interest to examine more closely. At high magnification, individual cells and cell nuclei can be studied.
Unfortunately, there is often a mismatch between the features visible at a certain magnification and the features actually appropriate for the diagnostic task at that level. Typically, the targeted situation is that overview tasks in low magnification would benefit from features expressed at high magnification. It is important to note that the high magnification features may be visually obvious at that level and no complicated analysis is required to identify them, but they may still be suppressed in the subsampling process producing the low magnification view. One example is hotspot quantification. It is common that diagnostic study or measurements should be carried out at the region of the image where there is the highest concentration of stained cells, for instance in Immunohistochemistry (IHC) staining of cancer cell nuclei. Thus, the first step is to identify this hotspot. This task is typically carried out at low magnification, since it is more efficient to have the overview of the entire slide at once. At high magnification, the targeted nuclei stand out as distinctly colored. At low magnification, however, the cells merge into a bland blur where it can be very difficult to assess cell concentration levels. Another relevant situation that can be improved is high magnification image generation for more efficient analysis.